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Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive datasets that focus on the dynamics of proteins, which are crucial for understanding protein function, folding, and interactions. To address this critical gap, we introduce mdCATH, a dataset generated through an extensive set of all-atom molecular dynamics simulations of a diverse and representative collection of protein domains. This dataset comprises all-atom systems for 5,398 domains, modeled with a state-of-the-art classical force field, and simulated in five replicates each at five temperatures from 320 K to 450 K. The mdCATH dataset records coordinates and forces every 1 ns, for over 62 ms of accumulated simulation time, effectively capturing the dynamics of the various classes of domains and providing a unique resource for proteome-wide statistical analyses of protein unfolding thermodynamics and kinetics. We outline the dataset structure and showcase its potential through four easily reproducible case studies, highlighting its capabilities in advancing protein science., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

The interpretation of ligand-target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, with the employment of both network-based approaches and Markov state models. We demonstrate the method by comprehensively mapping a large dataset of 640 μs of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand-target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design., (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/).)

The extracellular loop 2 (ECL2) is the longest and the most diverse loop among class A G protein-coupled receptors (GPCRs). It connects the transmembrane (TM) helices 4 and 5 and contains a highly conserved cysteine through which it is bridged with TM3. In this paper, experimental ECL2 structures were analyzed based on their sequences, shapes, and intramolecular contacts. To take into account the flexibility, we incorporated into our analyses information from the molecular dynamics trajectories available on the GPCRmd website. Despite the high sequence variability, shapes of the analyzed structures, defined by the backbone volume overlaps, can be clustered into seven main groups. Conformational differences within the clusters can be then identified by intramolecular interactions with other GPCR structural domains. Overall, our work provides a reorganization of the structural information of the ECL2 of class A GPCR subfamilies, highlighting differences and similarities on sequence and conformation levels.

SCoV2-MD (www.scov2-md.org) is a new online resource that systematically organizes atomistic simulations of the SARS-CoV-2 proteome. The database includes simulations produced by leading groups using molecular dynamics (MD) methods to investigate the structure-dynamics-function relationships of viral proteins. SCoV2-MD cross-references the molecular data with the pandemic evolution by tracking all available variants sequenced during the pandemic and deposited in the GISAID resource. SCoV2-MD enables the interactive analysis of the deposited trajectories through a web interface, which enables users to search by viral protein, isolate, phylogenetic attributes, or specific point mutation. Each mutation can then be analyzed interactively combining static (e.g. a variety of amino acid substitution penalties) and dynamic (time-dependent data derived from the dynamics of the local geometry) scores. Dynamic scores can be computed on the basis of nine non-covalent interaction types, including steric properties, solvent accessibility, hydrogen bonding, and other types of chemical interactions. Where available, experimental data such as antibody escape and change in binding affinities from deep mutational scanning experiments are also made available. All metrics can be combined to build predefined or custom scores to interrogate the impact of evolving variants on protein structure and function., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd (http://gpcrmd.org/), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyze and share GPCR MD data. GPCRmd originates from a community-driven effort to create an open, interactive and standardized database of GPCR MD simulations.

Mutations in the gelsolin protein are responsible for a rare conformational disease known as AGel amyloidosis. Four of these mutations are hosted by the second domain of the protein (G2): D187N/Y, G167R and N184K. The impact of the latter has been so far evaluated only by studies on the isolated G2. Here we report the characterization of full-length gelsolin carrying the N184K mutation and compare the findings with those obtained on the wild type and the other variants. The crystallographic structure of the N184K variant in the Ca 2+ -free conformation shows remarkable similarities with the wild type protein. Only minimal local rearrangements can be observed and the mutant is as efficient as the wild type in severing filamentous actin. However, the thermal stability of the pathological variant is compromised in the Ca 2+ -free conditions. These data suggest that the N to K substitution causes a local disruption of the H-bond network in the core of the G2 domain. Such a subtle rearrangement of the connections does not lead to significant conformational changes but severely affects the stability of the protein.

HTMD is a programmable scientific platform intended to facilitate simulation-based research in molecular systems. This paper presents the functionalities of HTMD for the preparation of a molecular dynamics simulation starting from PDB structures, building the system using well-known force fields, and applying standardized protocols for running the simulations. We demonstrate the framework's flexibility for high-throughput molecular simulations by applying a preparation, building, and simulation protocol with multiple force-fields on all of the seven hundred eukaryotic membrane proteins resolved to-date from the orientation of proteins in membranes (OPM) database. All of the systems are available on www.playmolecule.org .

Protein preparation is a critical step in molecular simulations that consists of refining a Protein Data Bank (PDB) structure by assigning titration states and optimizing the hydrogen-bonding network. In this application note, we describe ProteinPrepare, a web application designed to interactively support the preparation of protein structures. Users can upload a PDB file, choose the solvent pH value, and inspect the resulting protonated residues and hydrogen-bonding network within a 3D web interface. Protonation states are suggested automatically but can be manually changed using the visual aid of the hydrogen-bonding network. Tables and diagrams provide estimated pK a values and charge states, with visual indication for cases where review is required. We expect the graphical interface to be a useful instrument to assess the validity of the preparation, but nevertheless, a script to execute the preparation offline with the High-Throughput Molecular Dynamics (HTMD) environment is also provided for noninteractive operations.

Bio-molecular dynamics (MD) simulations based on graphical processing units (GPUs) were first released to the public in the early 2009 with the code ACEMD. Almost 8 years after, applications now encompass a broad range of molecular studies, while throughput improvements have opened the way to millisecond sampling timescales. Based on an extrapolation of the amount of sampling in published literature, the second timescale will be reached by the year 2022, and therefore we predict that molecular dynamics is going to become one of the main tools in drug discovery in both academia and industry. Here, we review successful applications in the drug discovery domain developed over these recent years of GPU-based MD. We also retrospectively analyse limitations that have been overcome over the years and give a perspective on challenges that remain to be addressed., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

Summary: Computer simulations are giving way to more complex and accurate studies of biological membranes by molecular dynamics (MD) simulations. The analysis of MD trajectories comprises the biophysical characterization of membrane properties or the study of protein-lipid interactions and dynamics. However, there is a lack of automated tools to analyse MD simulations of complex membrane or membrane-protein systems. Here we present MEMBPLUGIN, a plugin for the Visual Molecular Dynamics package that provides algorithms to measure a host of essential biophysical properties in simulated membranes. MEMBPLUGIN features are accessible both through a user-friendly graphical interface and as command-line procedures to be invoked in analysis scripts., Availability and Implementation: MEMBPLUGIN is a VMD extension written in Tcl. Multi-platform source code, documentation and tutorials are freely available at http://membplugin.sourceforge.net., Contact: toni.giorgino@isib.cnr.it or jana.selent@upf.edu, Supplementary Information: Supplementary data are available at Bioinformatics online.

A goal in the kinetic characterization of a macromolecular system is the description of its slow relaxation processes via (i) identification of the structural changes involved in these processes and (ii) estimation of the rates or timescales at which these slow processes occur. Most of the approaches to this task, including Markov models, master-equation models, and kinetic network models, start by discretizing the high-dimensional state space and then characterize relaxation processes in terms of the eigenvectors and eigenvalues of a discrete transition matrix. The practical success of such an approach depends very much on the ability to finely discretize the slow order parameters. How can this task be achieved in a high-dimensional configuration space without relying on subjective guesses of the slow order parameters? In this paper, we use the variational principle of conformation dynamics to derive an optimal way of identifying the "slow subspace" of a large set of prior order parameters - either generic internal coordinates or a user-defined set of parameters. Using a variational formulation of conformational dynamics, it is shown that an existing method-the time-lagged independent component analysis-provides the optional solution to this problem. In addition, optimal indicators-order parameters indicating the progress of the slow transitions and thus may serve as reaction coordinates-are readily identified. We demonstrate that the slow subspace is well suited to construct accurate kinetic models of two sets of molecular dynamics simulations, the 6-residue fluorescent peptide MR121-GSGSW and the 30-residue intrinsically disordered peptide kinase inducible domain (KID). The identified optimal indicators reveal the structural changes associated with the slow processes of the molecular system under analysis.

The PLUMED consortium unifies developers and contributors to PLUMED, an open-source library for enhanced-sampling, free-energy calculations and the analysis of molecular dynamics simulations. Here, we outline our efforts to promote transparency and reproducibility by disseminating protocols for enhanced-sampling molecular simulations.